Accuracy of hospital morbidity data and the performance of comorbidity scores as predictors of mortality

Abstract Objectives The main objectives of this study were to validate the hospital morbidity data (HMD) and to compare the performance of three comorbidity adjusting methods in predicting 1-year and 5-year all-cause mortality in a male general hospital population in Western Australia (WA). Study Design and Setting Population-based data were integrated with WA-linked data system. Deyo–Charlson Index, Enhanced-Charlson Index, and Elixhauser's method measured comorbidity. Mortality was modeled using Cox regression, and model discrimination was assessed by Harrell's C statistics. Results The HMD were most likely to identify major comorbidities, such as cancer, myocardial infarction, diabetes mellitus, and major operations. The presence of comorbidity was independently associated with an increased risk of adverse outcomes. All models achieved acceptable levels of discrimination (Harrell's C: 0.70–0.76). The Enhanced-Charlson Index matched the Deyo–Charlson Index in predicting mortality. Elixhauser's method outperformed the other two. Including information from past admissions achieved nonsignificant improvement in model discrimination. A dose-response effect was observed in the effect of repeated episodes on risk of 5-year mortality. Conclusion Comorbidities diagnosed at different points in time may have different associations with the risk of adverse outcomes. More research is required to integrate the effect of repeated episodes in currently used methods that measure and adjust for comorbidity.